Naming scheme

wg1.0.e == “western gesture 1, condition 0, eastern viewer culture” eg4.2.w == “eastern gesture 4 condition 2, western viewer culture”

Part One: Overlapping Metaphors

Load the data

Western Participants

Load this straight from AMT template.

Load this from psytoolkit template.

Eastern Participants

Individual resposne graphs.

Checking correlations for question pooling to form response domains.

some things should correlate, others should not. IE the questions that go together should correlate. accessible <–> open confident <–> sure conflict <–> tension dominant <–> control goal <–> worktogether many <–> members

So now we group the metaphor measures by group and facet wrap by that.

Correlation Matrix of questions

Get wrapped plot of correlations across conditions for same gesture

Violin plots of responses to questions

Density plot

Putting it all together.

Now we can access all of the cultural-independent responses through the variables from above: all_dat$overlays[["d0_overlay"]]: the overlayed violin plot of related questions. Illustrates density overlay aka a nice vis of correlation of questions all_dat$correlation_matrix: the correlation matrices that visualize the above as well. all_dat$violin_density_question: the violin plot of all question distributions across gesture conditions. all_dat$violin_density_grouped_overlay: the violin plot of all group distributions across gesture conditions, but overlayed. all_dat$violin_density_grouped: the violin plot of group distributions across gesture conditions. all_dat$density_grouped: density plot of group distributions (density of responses for likert category) across gesture conditions. all_dat$density_question: density plot of all question distributions (density of responses for likert category) across gesture conditions.

if you want to plot all of the overlays nicely you can do this:

Build and review the group correlations.

We are correlating how well the questions map to one another in order to determine whether it is appropriate to pool the responses into the response domains through which we can analyze the cross-cultural results.

And the result is a correlation matrix of the questions for the different groups of participants. Western Gestures, Western/Eastern Participants

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Eastern Gestures, Western/Eastern Participants

To get the individual ones (per gesture, say) you can do this to position the w/e side by side.

And finally, see all of them together with

Stats

In order to determine whether any of these differences are significant (aka, did people interpret different things from each of the different gesture conditions, which, because we, too, are people, we know they did) we need to see what the significant differences between rankings in each gesture and condition are.

Cleaning and discerning differences using t and mann-whitney.

ANOVAs not necessarily appropriate because it’s non-continuous, ordered categorical data.

T-tests for mean differences in averages.

## Warning in wilcox.test.default(x = c(3L, 4L, 4L, 4L, 5L, 7L, 6L, 1L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 4L, 4L, 4L, 5L, 7L, 6L, 1L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(5L, 3L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 2L, 5L, 4L, 2L, 7L, 6L, 5L, 1L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 2L, 5L, 4L, 2L, 7L, 6L, 5L, 1L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(2L, 5L, 2L, 5L, 2L, 6L, 4L, 5L, 3L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 3L, 4L, 3L, 4L, 7L, 4L, 7L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 3L, 4L, 3L, 4L, 7L, 4L, 7L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 4L, 5L, 3L, 4L, 4L, 6L, 5L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 6L, 4L, 7L, 6L, 3L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 6L, 4L, 7L, 6L, 3L, 4L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(5L, 5L, 4L, 4L, 5L, 2L, 6L, 5L, 5L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 6L, 5L, 3L, 4L, 7L, 7L, 3L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(3L, 6L, 5L, 3L, 4L, 7L, 7L, 3L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(6L, 4L, 5L, 5L, 6L, 3L, 4L, 4L, 2L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 4L, 6L, 3L, 6L, 5L, 6L, :
## cannot compute exact p-value with ties
group cond1 cond2 p sig
conflict cond2 cond1 0.00000 ***
conflict original cond2 0.00000 ***
unity cond2 cond1 0.00004 ***
unity original cond2 0.00329 **
group cond1 cond2 p sig
openness cond2 cond1 0.02861 *
openness original cond2 0.01653 *
conflict cond2 cond1 0.00000 ***
conflict original cond2 0.00000 ***
unity cond2 cond1 0.00002 ***
unity original cond2 0.00025 ***
group cond1 cond2 p sig
conflict cond1 original 0.00018 ***
group cond1 cond2 p sig
conflict cond1 original 0.00007 ***
conflict cond2 cond1 0.04688 *
size cond1 original 0.08259 *
group cond1 cond2 p sig
openness cond2 cond1 0.07005 *
group cond1 cond2 p sig
openness cond2 cond1 0.00376 **
openness original cond2 0.00756 *
control cond2 cond1 0.01136 *
size cond2 cond1 0.02093 *
group cond1 cond2 p sig
group cond1 cond2 p sig
group cond1 cond2 p sig
group cond1 cond2 p sig
conflict original cond2 0.06143 *
unity cond2 cond1 0.00965 *
The above s hows that t tests a nd mann-wh itney-wilcox have the same significance results.

Individual Response Patterns

Cool so we have all this but what do individuals actually perceive – multiple interpretations, or one predominant one?

Looking at individual response styles through pie chart.

– highly is 5,6,7 for likert - people who ranked X & Y highly - people who ranked X highly (but not Y) - people who ranked Y highly (but not X) - people who did neither

For example, we can visualize the difference between how individuals in western culture rated different response domains comapred to eastern cultures as below: #### Comparing cultures Notice how the amount rating “both” and “neither” changes significantly between the cultures. This indicates that the different cultures pick up different semantic cues from the gesture.

There is not a pattern in the responses in terms of one population rating “both” more often than the other. That is to say, neither culture is “more predisposed” to have multiple interpretations.

Compare interpretations between cultures

Reorganize data to have viewer_culture

Violin plot comparisons

Can only plot by one thing at a time (i.e. for a single gesture condition then visualize across metaphor measures, or for a single metaphor measure then visualize across conditions)

Now the plots live in things like wg1_violin_culture_comparisons$c0_violin_comparison

These visualizations are not very useful though, because they don’t do a good job of emphasizing the differences between cultures.

Stats

Now all that data lives in things like wg1_total_cultural_comparison_tables$c0_comparison that look like this: WG1-0 (this name isn’t here in the actual version, we just know from the naming scheme)

WG1

Original, Cond1, Cond2 Basically, “were there significant differences between western and eastern viewers in this set of conditions?”

As we said before, this visualization does a pretty lousy job of actually highlighting the differences between the cultures.

Experimental power

Short Answer: yes, power is high enough

Example usage:

cond_power <- calculate_power_for_condition(wg2_total, "conflict", "original")

Then you get a variable called cond_power you can use to see the actual power through cond_power$power. In this case our power is 0.9946474

Analyzing with high/med/low

We’re bucketing our results into high/medium/low to analyze the differences in interpretation, because either a high or low response essentially means the individual had an opinion or an interpretation of the gesture, whereas medium responses suggested no interpretation.

And now actually analyze the stats we’ve gathered

  1. Tell if proportion between groups are sig.

And here are all of the results of the cultural comparison by buckets.

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 0 *** *** 0.058 1e-04 *** *** 43 21 14 32 3 27
cond1 conflict 0.0346 * 1 0.0298 * 15 35 9 13 36 32
cond1 control 6e-04 ** ** 0.0057 * * 0.5346 33 20 12 35 15 25
cond1 unity 1e-04 *** *** 0.0035 ** * 0.1194 47 36 4 22 9 22
cond1 openness 0.0083 * * 0.0865 0.3908 27 18 9 23 24 39
cond1 size 0.5599 0.6175 0.2445 24 27 15 16 21 37
cond2 certainty 0.0479 * 0.4545 0.1722 43 32 16 22 13 22
cond2 conflict 0.0085 * * 0.0139 * * 0.4186 61 49 3 14 8 13
cond2 control 0.0033 ** * 0.1334 0.1164 41 24 14 24 17 28
cond2 unity 0.0923 0.073 9e-04 ** ** 27 40 8 18 37 18
cond2 openness 0.7084 0.1722 0.4334 10 8 13 22 49 46
cond2 size 0.6396 0.0324 * 0.1971 35 33 11 24 26 19
original certainty 0.0649 0.1297 0.5753 50 27 10 14 16 15
original conflict 0.2814 0.4337 0.7235 31 29 13 6 32 21
original control 0.1572 0.4425 0.0325 * 32 16 16 8 28 32
original unity 0.1984 0.5472 0.4563 49 29 12 12 15 15
original openness 0.1181 0.0998 1 19 22 26 11 31 23
original size 1 0.3055 0.3962 35 26 19 9 22 21

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 0.0911 0.0378 * 1 56 33 9 16 19 15
cond1 conflict 0.0028 ** * 0.7162 2e-04 *** ** 40 47 9 9 35 8
cond1 control 0.0022 ** * 0.7232 2e-04 *** ** 30 40 14 13 40 11
cond1 unity 2e-04 *** ** 1e-04 *** *** 0.7752 52 19 10 26 22 19
cond1 openness 1e-04 *** *** 0.901 0.0022 ** * 31 5 23 19 30 40
cond1 size 0.6072 0.7532 0.9821 24 15 18 16 42 33
cond2 certainty 0.0114 * * 0.0824 0.2919 46 34 12 24 12 20
cond2 conflict 0.3227 0.0272 * 0.6936 46 44 4 15 20 19
cond2 control 0.1787 0.4891 0.4864 41 36 12 18 17 24
cond2 unity 0.0614 0.0638 0.8397 44 36 10 22 16 20
cond2 openness 0.0018 ** * 0.3322 0.1084 24 9 14 22 32 47
cond2 size 0.1245 0.9083 0.2102 32 25 12 15 26 38
original certainty 0.8555 1 0.8744 37 26 17 11 22 13
original conflict 3e-04 *** ** 0.9345 0 *** *** 56 20 15 11 5 19
original control 6e-04 ** ** 0.4952 0.005 ** * 43 12 16 14 17 24
original unity 0.8555 0.1763 0.079 37 26 15 16 24 8
original openness 0.5125 0.5125 0.1726 19 16 19 16 38 18
original size 0.0631 0.1377 0.6245 43 19 13 15 20 16

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 0.1589 0.4753 0.4942 40 26 19 21 17 19
cond1 conflict 0.56 0.2933 0.9336 30 22 11 15 35 29
cond1 control 0 *** *** 0.0142 * * 0.0173 * 52 21 6 16 18 29
cond1 unity 0.0539 0.0973 0.67 49 31 13 20 14 15
cond1 openness 0.3426 0.8805 0.2483 25 16 18 14 33 36
cond1 size 0.2576 0.5502 0.0657 44 31 13 8 19 27
cond2 certainty 0.002 ** * 0.1306 0.0381 * 60 23 22 22 10 15
cond2 conflict 0.2784 0.386 0.0687 22 20 15 14 55 26
cond2 control 0.2462 0.6029 0.5826 39 19 17 14 36 27
cond2 unity 6e-04 ** ** 0.0052 * * 0.2376 73 31 12 20 7 9
cond2 openness 7e-04 ** ** 0.0084 * * 0.2899 53 17 14 21 25 22
cond2 size 0.9553 0.4238 0.5765 63 40 14 13 15 7
original certainty 0.0781 0.3108 0.4022 43 22 20 20 11 12
original conflict 0.6794 0.4261 0.2015 17 15 20 19 37 20
original control 0.0127 * * 0.0714 0.4055 38 15 11 16 25 23
original unity 0.6794 0.5632 1 57 39 11 11 6 4
original openness 0.4476 0.6444 0.9264 25 14 22 19 27 21
original size 1 0.2268 0.3044 52 38 6 9 16 7

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 1 0.4388 0.2649 28 51 12 16 4 15
cond1 conflict 1 0.313 0.4646 14 25 8 23 22 34
cond1 control 0.2899 0.2459 0.031 * 16 21 14 17 14 44
cond1 unity 0.6181 1 0.4089 28 47 13 24 3 11
cond1 openness 0.2317 0.8889 0.0872 25 36 12 20 7 26
cond1 size 0.0037 ** * 0.9548 3e-04 *** ** 33 38 9 15 2 29
cond2 certainty 0.8704 1 0.8924 25 32 18 25 11 17
cond2 conflict 0.0891 0.1923 0.606 18 37 13 10 23 27
cond2 control 0.0275 * 0.4862 0.0034 ** * 24 18 17 18 13 38
cond2 unity 0.5348 0.9617 0.6005 32 49 12 15 10 10
cond2 openness 0.3335 0.8815 0.1925 19 19 22 28 13 27
cond2 size 0.4612 0.9246 0.5699 35 42 9 14 10 18
original certainty 0.8583 0.1597 0.259 39 48 10 22 13 10
original conflict 0.9317 0.1292 0.3555 16 19 7 18 39 43
original control 0.1413 1 0.1618 20 16 13 16 29 48
original unity 0.0728 0.0803 0.7948 51 54 5 16 6 10
original openness 0.7672 0.7159 0.3334 34 47 13 20 15 13
original size 0.0768 0.804 0.0724 44 44 12 18 6 18

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 1e-04 *** *** 0.0891 0.0396 * 24 20 8 31 6 29
cond1 conflict 0.0824 0.652 0.3354 14 16 6 17 18 47
cond1 control 0.411 0.0242 * 0.0047 ** * 13 20 14 13 11 47
cond1 unity 0.0026 ** * 1 0.0011 ** ** 32 43 5 12 1 25
cond1 openness 0.0157 * * 0.8106 0.0018 ** * 23 28 12 22 3 30
cond1 size 0.2775 0.1637 0.0079 * * 17 26 15 20 6 34
cond2 certainty 0.8053 0.6243 0.2652 23 28 18 20 7 16
cond2 conflict 0.0204 * 0.0259 * 0.8474 23 16 5 19 20 29
cond2 control 0.6043 0.7941 0.3568 15 16 17 20 16 28
cond2 unity 0.6374 0.8659 0.8659 30 36 9 14 9 14
cond2 openness 0.9781 0.8811 0.6857 22 28 15 18 11 18
cond2 size 0.0459 * 0.7978 0.0424 * 28 24 16 24 4 16
original certainty 0.0285 * 0.2263 0.2906 27 33 8 25 9 26
original conflict 0.2724 0.4612 0.0944 7 22 4 13 33 49
original control 1 0.3986 0.5591 10 20 12 16 22 48
original unity 0.3698 0.5061 0.8476 31 51 7 19 6 14
original openness 0.3576 0.0235 * 0.4646 26 41 3 21 15 22
original size 0.3777 0.0339 * 0.0017 ** ** 20 30 18 18 6 36

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 1 0.9697 0.8838 36 25 17 11 15 12
cond1 conflict 0.0838 0.8488 0.1177 42 21 12 10 14 17
cond1 control 0.3007 0.6323 0.0535 39 22 18 10 11 16
cond1 unity 0.9318 0.8838 1 40 27 15 12 13 9
cond1 openness 0.1537 0.5534 0.6821 17 6 18 16 33 26
cond1 size 0.0224 * 0.3459 0.1839 34 13 20 19 14 16
cond2 certainty 0.6006 0.8576 0.3768 36 21 33 18 39 15
cond2 conflict 0.7811 0.8301 0.5149 54 25 21 9 33 20
cond2 control 0.9518 0.0242 * 0.0835 32 17 33 7 43 30
cond2 unity 0.7769 1 0.6432 66 31 19 9 23 14
cond2 openness 0.453 0.4344 0.086 42 17 36 14 30 23
cond2 size 0.541 0.5587 1 53 30 28 11 27 13
original certainty 1 0.7932 0.6508 31 28 18 18 17 12
original conflict 0.5622 0.3002 0.9348 26 19 10 14 30 25
original control 0.2872 0.2872 0.0451 * 19 11 19 11 28 36
original unity 0.3057 1 0.2651 41 42 11 9 14 7
original openness 0.3667 0.6189 0.8167 20 23 22 16 24 19
original size 0.9118 0.3746 0.5373 29 27 23 15 14 16

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 5e-04 ** ** 0.0017 ** ** 0.7271 53 6 25 19 16 7
cond1 conflict 1 4e-04 *** ** 0.0075 * * 27 9 10 13 57 10
cond1 control 0.4658 0.1465 0.6905 32 8 21 12 41 12
cond1 unity 0.0652 0.0039 ** * 0.8524 71 18 8 10 15 4
cond1 openness 0.6125 0.48 1 33 9 27 12 34 11
cond1 size 6e-04 ** ** 0.0279 * 0.082 64 10 18 13 12 9
cond2 certainty 0.6776 1 0.6765 43 20 27 11 32 11
cond2 conflict 0.1919 0.1226 1 60 19 16 12 26 11
cond2 control 0.0749 0.6765 0.0117 * * 42 10 37 13 23 19
cond2 unity 1 0.2144 0.2673 51 21 18 12 33 9
cond2 openness 0.917 1 1 17 6 43 18 42 18
cond2 size 0.1994 0.6701 0.3609 55 17 31 15 16 10
original certainty 1 1 1 49 15 20 6 23 7
original conflict 0.248 0.8822 0.1172 53 12 13 3 26 13
original control 0.0753 0.8204 0.1471 46 8 19 7 27 13
original unity 1 0.3634 0.3522 55 17 14 7 23 4
original openness 0.8032 0.6218 0.3957 22 8 20 8 50 12
original size 0.9885 1 1 45 13 21 7 26 8

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 0.8133 0.7804 1 47 27 18 8 13 7
cond1 conflict 0.0922 0.9754 0.0601 38 13 9 4 31 25
cond1 control 1 0.8757 0.9694 23 12 18 11 37 19
cond1 unity 0.5737 1 0.6923 59 29 6 4 13 9
cond1 openness 0.3281 0.1143 0.7482 25 18 27 8 26 16
cond1 size 0.0812 0.6303 0.1848 46 17 16 11 16 14
cond2 certainty 0.0869 1 0.0523 51 17 20 10 13 13
cond2 conflict 0.0331 * 0.5933 0.0079 * * 24 20 14 9 46 11
cond2 control 0.5106 0.0188 * 0.0128 * * 34 13 17 1 33 26
cond2 unity 0.0777 0.2898 0.3277 70 27 6 6 8 7
cond2 openness 0.1619 0.6704 0.0444 * 31 9 30 12 23 19
cond2 size 0.1467 0.5703 0.3391 55 20 10 7 19 13
original certainty 0.4254 0.9092 0.1506 55 33 18 11 9 12
original conflict 0.4076 0.0042 ** * 0.0013 ** ** 24 21 9 18 49 17
original control 0.2202 0.1029 0.8893 33 16 19 21 30 19
original unity 0.8419 0.4262 0.6981 62 44 14 6 6 6
original openness 0.6097 0.561 1 25 14 20 17 37 25
original size 0.687 0.3017 0.7101 48 30 16 16 18 10

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condition group high_comp adjusted_high_comp med_comp adjusted_med_comp low_comp adjusted_low_comp w_high e_high w_med e_med w_low e_low
cond1 certainty 0.0522 0.2401 0.3146 45 36 20 7 15 5
cond1 conflict 0.9622 0.5901 0.8157 30 17 17 13 33 18
cond1 control 0.7424 0.4339 0.2162 32 17 23 10 25 21
cond1 unity 0.2214 0.6773 0.3581 58 29 13 10 9 9
cond1 openness 0.6554 0.0203 * 0.0026 ** * 26 13 36 11 18 24
cond1 size 0.0303 * 1 0.0026 ** * 61 27 13 7 6 14
cond2 certainty 0.5596 0.3919 0.9831 37 22 27 10 14 8
cond2 conflict 0.4032 0.4288 1 39 16 15 11 24 13
cond2 control 0.0023 ** * 0.7753 0.0026 ** * 46 11 20 12 12 17
cond2 unity 0.0536 0.2771 0.3078 51 18 15 12 12 10
cond2 openness 0.8792 0.8708 0.6179 22 10 28 13 28 17
cond2 size 0.0357 * 0.403 0.1287 54 19 13 10 11 11
original certainty 0.906 0.4798 0.2159 56 22 24 7 12 9
original conflict 3e-04 *** ** 0.2464 0.0446 * 16 19 25 6 51 13
original control 0.6191 0.2223 0.0587 35 12 31 8 26 18
original unity 0.0101 * * 0.8097 0.005 ** * 67 18 16 8 9 12
original openness 0.0798 0.8688 0.1552 33 7 38 17 21 14
original size 0.0092 * * 0.0877 0.2114 63 16 19 14 10 8

Additional questions:

Q: How do we tell whether people HAD an interpretation, or if it was uniform confusion? A: Look instead of “low,” and “high,” the union of the two – those who had interpretation.

The way to interpret this graph is the blues are the medium values, the pinks are the extreme (high+ ) values. The light is eastern, the dark is western. The blue being higher than the pinks indicate that there was NOT a strong interpretation – that is, more people ranked it 4 less than EITHER 1,2,3 or 5,6,7 – that is to say, they chose an interpretation! This is particularly clear in the case of the conflict condition for westerners in WG1 – note that there is a very strong clear interpretation for westerners, but not at all for easterners.

You can calculate and view all of them below:

X-Culture Scale Corrections

So for all of this, we are definitely using parceling, which is the process of combining individual scores to produce a new score (we combine two survey questions to form the category groups we analyze).

Ipsatization is the process of taking the mean of individual’s response for every question, and subtracting it from all of their responses, then using that dataset as that individual’s response. This doesn’t necessarily work for us because we specifically observe “extreme” responses – our Western participants rated things both higher and lower than eastern, whereas eastern stayed towards the mean. This means that this will probably not affect our results so much… but might be worth a try anyways?

Otherwise, we could try GRS construction and correction.

First let’s measure the extent to which we’re experiencing ARS/ERS.

Interestingly, we find a narrow range of responses for both western and eastern viewers (stdev=1.69 and 1.54 respectively). Both have significantly higher proportation of mid-scale responses (W=21.4%, E=25.3%) than expected.

However this still leaves open the possibility of within-subject correction… but that still doesn’t necessarily capture what we want to know. We’re specifically testing for cross-cultural differences, so correcting based on the mean doesn’t work, and stdevs aren’t sig diff. Therefore, it’s reasonable to assume the differences we observe are due to true differences in the cultural interpretations of these gestures.

More Questions

Q: Are westerners more likely overall to interpret conflict?

A: No. Actually, western avg for conflict = 3.94, whereas eastern = 3.97.

A continuation of that question… #### Q: In what categories did the differences occur?

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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
group sig_high_diffs sig_med_diffs sig_low_diffs total
certainty 5 1 1 7
conflict 4 3 5 12
control 7 2 7 16
openness 5 1 3 9
size 3 0 4 7
unity 5 4 3 12
A: So there is no particular category in whic h one culture wa s more apt to rate higher than the other.

Q: Did one cultural gesture result in more differences than the other?

## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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total differences in W gestures: 44 total differences in E gestures: 19 A: Yes, the western gestures, but there’s no objective “correct” interpretation so who cares.

Q: But who was higher?

## Warning: Column `condition` joining factor and character vector, coercing
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## Warning: Column `condition` joining factor and character vector, coercing
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## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
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## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
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## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
## into character vector
## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
## into character vector
## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
## into character vector
## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning: Column `condition` joining factor and character vector, coercing
## into character vector
## Warning: Column `group` joining factor and character vector, coercing into
## character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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## Warning in bind_rows_(x, .id): binding character and factor vector,
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##   [1]   0   2   4   6   8  10  12  14  16  18  20  22  24  26  28  30  32
##  [18]  34  36  38  40  42  44  46  48  50  52  54  56  58  60  62  64  66
##  [35]  68  70  72  74  76  78  80  82  84  86  88  90  92  94  96  98 100
##  [52] 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134
##  [69] 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168
##  [86] 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200 202
## [103] 204 206 208 210 212 214 216 218 220 222 224 226 228 230 232 234 236
## [120] 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270
## [137] 272 274 276 278 280 282 284 286 288 290 292 294 296 298 300 302 304
## [154] 306 308 310 312 314 316 318 320 322 324

Q: What did our score distributions actually look like?

A: Like this, aka not too different:

## Saving 7 x 5 in image

Q: Was there a significant difference from uniform distribution in terms of differences between groups?

## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  tdrw
## D = 0.42857, p-value = 0.1106
## alternative hypothesis: two-sided
## Saving 7 x 5 in image

A: Yes, the overal general distribution of scores is basically the same… Furthermore, the difference should be a uniform distribution… and it is.

Q: What if we break down the score distribution by gesture?

I see……. it could be the case that although overall averages are the same, the gestures in which eastern folks had interpretations were fewer?

It’s a real pain in the keister to look at each individual distribution, and frankly I don’t think that’ll help us much. What I’m more interested in is the overall pattern.

Within-culture differences

Q: did our manipulations result in sig diffs within cultures?

A: Yes, the different gesture conditions led to differences within-culture that were often in the same direction, but sometimes different. Compare them like this:

A2: so, our manipulations were generally effective, especially in the direction we expected for western culture, but sometimes for eastern as well. However, it makes sense that they won’t always be the same because the overall picture is showing us that the atomic motion features of a gesture are interpreted differently by different cultures.

Q: how did our manipulations influence each culture?

build the table to look like: viewer_culture | original.mean | group | condition1 | condition 2 | p.diff.1 | p.diff.2 w 4.92 cert 5.17 4.62 0.284 0.259 e 4.27 cert 3.775 4.18 0.05 0.739

And also check directionality of differences

This is quite interesting! It shows that our manipulations only occasionally caused significant differences from the original gesture, and sometimes even that was in opposite directions!!

Let’s look at a few of them side-by-side

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.921 certainty 5.167 4.625 0.284 0.050 ^ * v wg1
eastern 4.268 certainty 3.775 4.184 0.259 0.739 v v wg1
western 3.855 conflict 3.133 5.889 0.030 0.440 * v ^ wg1
eastern 4.161 conflict 3.938 4.987 0.000 0.004 * v * ^ wg1
western 4.211 control 4.417 4.694 0.482 0.531 ^ ^ wg1
eastern 3.643 control 3.812 3.934 0.082 0.276 ^ ^ wg1
western 3.711 openness 3.900 2.986 0.500 0.017 ^ * v wg1
eastern 4.107 openness 3.500 3.105 0.004 0.000 * v * v wg1
western 4.276 size 4.217 4.292 0.841 0.224 v ^ wg1
eastern 4.179 size 3.837 4.197 0.958 0.941 v ^ wg1
western 5.105 unity 5.400 3.625 0.295 0.356 ^ v wg1
eastern 4.536 unity 4.275 4.355 0.000 0.529 * v v wg1

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.276 certainty 4.798 4.957 0.036 0.748 * ^ ^ wg2
eastern 4.540 certainty 4.453 4.308 0.006 0.377 * v v wg2
western 5.487 conflict 4.012 4.957 0.000 0.001 * v * v wg2
eastern 3.940 conflict 4.922 4.474 0.063 0.078 ^ ^ wg2
western 4.618 control 3.893 4.600 0.005 0.000 * v * v wg2
eastern 3.480 control 4.703 4.077 0.943 0.052 ^ ^ wg2
western 3.382 openness 4.012 3.657 0.014 0.001 * ^ * ^ wg2
eastern 3.780 openness 2.938 3.051 0.309 0.005 v * v wg2
western 4.421 size 3.702 4.171 0.008 0.013 * v * v wg2
eastern 4.120 size 3.406 3.564 0.374 0.042 v * v wg2
western 4.263 unity 4.702 4.929 0.076 0.034 ^ * ^ wg2
eastern 4.540 unity 3.984 4.321 0.014 0.392 * v v wg2

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.784 certainty 4.500 4.989 0.267 0.801 v ^ wg3
eastern 4.259 certainty 4.197 4.233 0.309 0.910 v v wg3
western 3.297 conflict 3.697 3.120 0.159 0.685 ^ v wg3
eastern 3.722 conflict 3.833 3.950 0.486 0.415 ^ ^ wg3
western 4.324 control 4.882 3.924 0.065 0.822 ^ v wg3
eastern 3.667 control 3.727 3.683 0.162 0.953 ^ ^ wg3
western 3.757 openness 3.684 4.739 0.794 0.377 v ^ wg3
eastern 3.722 openness 3.500 3.800 0.000 0.755 * v ^ wg3
western 5.027 size 4.579 5.446 0.129 0.002 v * ^ wg3
eastern 4.963 size 4.106 4.833 0.125 0.589 v v wg3
western 5.446 unity 4.908 5.554 0.030 0.048 * v * ^ wg3
eastern 5.000 unity 4.500 4.600 0.614 0.089 v v wg3

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.903 certainty 4.955 4.444 0.851 0.984 ^ v wg4
eastern 4.825 certainty 4.829 4.338 0.099 0.018 ^ * v wg4
western 3.145 conflict 3.545 3.907 0.231 0.067 ^ ^ wg4
eastern 3.325 conflict 3.780 4.068 0.019 0.007 * ^ * ^ wg4
western 3.726 control 4.045 4.463 0.280 0.115 ^ ^ wg4
eastern 3.163 control 3.537 3.351 0.012 0.451 * ^ ^ wg4
western 4.645 openness 4.659 4.111 0.959 0.005 ^ * v wg4
eastern 4.750 openness 4.159 3.784 0.050 0.000 * v * v wg4
western 5.210 size 5.432 4.778 0.393 0.081 ^ v wg4
eastern 4.600 size 4.195 4.527 0.107 0.752 v v wg4
western 5.306 unity 4.955 4.741 0.161 0.134 v v wg4
eastern 4.950 unity 4.646 4.851 0.044 0.630 * v v wg4

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.364 certainty 5.079 4.479 0.051 0.107 ^ ^ wg5
eastern 4.179 certainty 3.812 4.266 0.712 0.705 v ^ wg5
western 2.886 conflict 3.579 4.000 0.109 0.961 ^ ^ wg5
eastern 3.274 conflict 3.263 3.578 0.005 0.208 * v ^ wg5
western 3.318 control 4.158 3.917 0.027 0.474 * ^ ^ wg5
eastern 3.298 control 3.487 3.656 0.078 0.158 ^ ^ wg5
western 4.455 openness 4.947 4.271 0.171 0.332 ^ v wg5
eastern 4.214 openness 3.962 4.391 0.579 0.476 v ^ wg5
western 4.455 size 4.684 4.708 0.438 0.638 ^ ^ wg5
eastern 3.857 size 3.737 4.172 0.343 0.231 v ^ wg5
western 5.159 unity 5.895 4.875 0.019 0.034 * ^ * v wg5
eastern 4.857 unity 4.338 4.484 0.378 0.114 v v wg5

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.212 certainty 4.618 3.944 0.114 0.797 ^ v eg1
eastern 4.466 certainty 4.396 4.093 0.217 0.136 v v eg1
western 3.894 conflict 4.647 4.157 0.010 0.318 * ^ ^ eg1
eastern 3.862 conflict 4.188 4.185 0.312 0.291 ^ ^ eg1
western 3.636 control 4.912 3.713 0.000 0.002 * ^ * ^ eg1
eastern 3.190 control 4.229 3.278 0.752 0.782 ^ ^ eg1
western 3.939 openness 3.368 4.231 0.027 0.003 * v * ^ eg1
eastern 4.000 openness 3.167 3.685 0.176 0.294 v v eg1
western 4.303 size 4.529 4.380 0.335 0.055 ^ ^ eg1
eastern 4.276 size 3.771 4.444 0.709 0.541 v ^ eg1
western 4.773 unity 4.691 4.769 0.778 0.113 v v eg1
eastern 4.862 unity 4.438 4.648 0.988 0.405 v v eg1

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.446 certainty 4.723 4.098 0.208 0.276 ^ v eg2
eastern 4.357 certainty 4.000 4.310 0.167 0.893 v v eg2
western 4.446 conflict 3.255 4.686 0.000 0.992 * v ^ eg2
eastern 3.964 conflict 3.969 4.310 0.376 0.437 ^ ^ eg2
western 4.391 control 3.713 4.206 0.004 0.632 * v v eg2
eastern 3.607 control 3.812 3.714 0.418 0.802 ^ ^ eg2
western 3.370 openness 3.979 3.441 0.005 0.923 * ^ ^ eg2
eastern 3.786 openness 3.750 3.452 0.741 0.312 v v eg2
western 4.293 size 4.894 4.667 0.005 0.582 * ^ ^ eg2
eastern 4.214 size 4.000 4.095 0.096 0.759 v v eg2
western 4.685 unity 5.309 4.275 0.007 0.879 * ^ v eg2
eastern 4.643 unity 4.688 4.452 0.092 0.494 ^ v eg2

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.963 certainty 4.679 4.786 0.169 0.799 v v eg3
eastern 4.714 certainty 4.786 4.150 0.420 0.087 ^ v eg3
western 3.317 conflict 4.000 3.179 0.018 0.047 * ^ * v eg3
eastern 4.054 conflict 3.476 4.425 0.622 0.236 v ^ eg3
western 4.037 control 3.795 3.905 0.343 0.173 v v eg3
eastern 3.946 control 3.571 3.300 0.627 0.052 v v eg3
western 3.707 openness 3.987 3.988 0.258 0.142 ^ ^ eg3
eastern 3.696 openness 4.143 3.475 0.258 0.494 ^ v eg3
western 4.707 size 4.718 4.750 0.965 0.034 ^ * ^ eg3
eastern 4.571 size 4.000 4.275 0.854 0.353 v v eg3
western 5.476 unity 5.282 5.548 0.416 0.750 v ^ eg3
eastern 4.982 unity 4.881 4.875 0.746 0.716 v v eg3

viewer_culture original.mean group condition.1.mean condition.2.mean p.1 p.2 p1.sig.diff p1.diff p2.sig.diff p2.diff gesture_name
western 4.804 certainty 4.612 4.718 0.321 0.252 v v eg4
eastern 4.579 certainty 4.917 4.425 0.669 0.629 ^ v eg4
western 3.337 conflict 3.900 4.333 0.010 0.473 * ^ ^ eg4
eastern 4.158 conflict 3.938 4.000 0.000 0.627 * v v eg4
western 4.185 control 4.162 4.731 0.921 0.506 v ^ eg4
eastern 3.605 control 3.833 3.600 0.007 0.989 * ^ v eg4
western 4.207 openness 4.188 3.936 0.918 0.626 v v eg4
eastern 3.579 openness 3.729 3.600 0.170 0.948 ^ ^ eg4
western 5.141 size 5.088 4.987 0.776 0.732 v v eg4
eastern 4.316 size 4.208 4.425 0.461 0.719 v ^ eg4
western 5.261 unity 4.975 4.923 0.154 0.126 v v eg4
eastern 4.316 unity 4.792 4.400 0.128 0.791 ^ ^ eg4
A: We see that ou r manipulations led to chang es in different are as all across the b oard.